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RUSLE has been modified from the Universal Soil Loss Equation (USLE) by adapting the input factors to the local conditions (McCool et al. 1995; Renard et al. 1996).
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Although the use of different velocity thresholds can lead to different outcomes from the detection algorithms, it is possible to remove a number of artifactual fixations (this is especially important in noisy data sets) by also adapting the input parameters for the post hoc validation criteria (explained below) and manually fixing the fixations that were not detected correctly.
The ultimate aim of adapting the input parameters and applying certain post hoc validation criteria is to obtain the most accurate outcome by the algorithms alone and, thus, reduce the hand-coding time during the subsequent step.
The performance of these approaches can be enhanced by adapting the computational procedure with regards to the input of HVS preferences.
They achieve computational efficiency by adapting the sampling frequency and the filter order according to the input signal local variations.
This can be done by adapting the library to a particular combination of an input object model and an output tabular format.
Our cellular automaton allows the examination of different interactions by adapting the truth table in Table 3, and the effect of external inputs, as represented by the parameters of phosphorylation and dephosphorylation.
On the other hand, it is possible to adapt the input parameters according to the level of noise on a participant-by-participant basis (e.g., Nyström & Holmqvist, 2010).
At higher levels of λ, the INMB changes most when adapting the Dutch input data to the German input data.
At lower levels of λ, the INMB changes most when adapting the Dutch input data to the UK input data.
This paper aims to achieve power efficiency by intelligently adapting the processing activity to the input signal local characteristics.
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